The present disclosure relates generally to wellbore and completion design operations and, more particularly, to data storage, modeling, and design for well planning, drilling, and completion operations.
Hydrocarbons, such as oil and gas, are produced from subterranean reservoir formations that may be located onshore or offshore. The processes involved in recovering hydrocarbons from a reservoir are becoming increasingly complex. Subterranean production is a highly expensive and extensive endeavor and the industry generally relies heavily upon educated predictions of reservoir conditions to characterize the reservoir prior to making substantial investments to optimize well placement within the reservoir, optimize production of hydrocarbons, and performing the necessary steps to produce, process and transport the hydrocarbons from the reservoir.
Planning for and performing the production steps generally requires the manipulation of large amount of information and generation of design and uncertainty modeling tasks. Simulators that predict the manner for developing a design or modeling of reservoirs are separately maintained such that no information is traditionally shared between individual simulations associated with a particular reservoir analysis. For example, planning for a drilling operation may include retrieving information from a relational database and generating relational models that represent the characteristics of the subterranean formation to use to base the wellbore and completion design. These simulations can provide an output with an uncertainty for various manners of design and can be utilized by reservoir engineers to make a number of observations and predictions about, for example, the multi-phase flow of oil, gas, and water in a subterranean reservoir. Engineers can further simulate various wellbore and completion designs based on the various uncertainty models to determine one or more improved or optimal location and design of the wellbore to optimize the recoveries of such resources. These are not the only types of parameters taken into account in building a completion design.
Other completion design may factor in Geo-mechanical technologies that characterize rock properties to predict the state of earth stresses and natural fractures and or faults in a formation.
These simulations can be computationally intensive and yield results with certain uncertainty (typically referred to as uncertainty models). But once a completion design has been selected, typically, this information is not shared outside of the individual design which prevents expanding the field of knowledge for completions design technology uniformly across a broader set.
Similarly, measurements may be generated during the drilling operation and used to augment the relational model. The generated measurements may also be stored in the relational database for use at a later time.
Typical relational databases and models are complex and difficult to generalize to multiple reservoirs and often only contain select information making it necessary to access information from multiple sources. For instance, the data within the relational database is generally tied to gridded reservoir volumes within the formation in which the data was generated. The relational models are generated from this data, making it difficult to generalize the data outside of the formation in which it was generated. In many instances, this means that complex analytical and design solutions must be generated from scratch each time they are run, which is inefficient with respect to both labor and computational resources.
In uncertainty modeling, an engineer, may generate hundreds of simulation models that result in various output. The engineer may then select a particular simulation model or set of models in designing the completion design and operation. However, the simulations and results are generally stored locally by the engineer and are inaccessible and unusable in any subsequent well design. Moreover, such simulations have no readily available method of post-completion testing, by allowing for a set of resulting parameters to match up to the original simulation model to determine the relationship between the simulation and actual operation of a design.
While embodiments of this disclosure have been depicted and described and are defined by reference to exemplary embodiments of the disclosure, such references do not imply a limitation on the disclosure, and no such limitation is to be inferred. The subject matter disclosed is capable of considerable modification, alteration, and equivalents in form and function, as will occur to those skilled in the pertinent art and having the benefit of this disclosure. The depicted and described embodiments of this disclosure are examples only, and not exhaustive of the scope of the disclosure.